环境科学最新文献

{"title":"[Mechanism of Transportation Capacity Influence on Carbon Emission of Transportation Sector at China Provincial Scale from a Spatial Perspectiv].","authors":"Yun-Yan Li, Xue-Ying Zhang","doi":"10.13227/j.hjkx.202311163","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311163","url":null,"abstract":"<p><p>Transportation carbon emissions （TCE） are a major contributor to emissions, and their emission reduction pathway can provide recommendations for the overall carbon emission reduction decision-making. Therefore, identifying the impact mechanism of transportation capacity on TCE through scientific methods has become an important foundation to respond to the national \"dual-carbon\" strategy. Based on the panel data of 30 provinces in China from 2012 to 2021, a dynamic spatial dubin model （SDM） was included to empirically analyze the dynamic spatial spillover effects of transportation capacity on carbon emissions from China's transportation sector, taking correlation and heterogeneity into account. The results were as follows： ① There was regional spatial correlation in carbon emissions from China's transportation sector, and this situation was becoming increasingly evident in time series. ② Transportation capacity had a positive impact on the carbon emission reduction of the local transport sector, which fluctuated, but the negative value was not less than -1.212%. There may have been an inverted U-shaped EKC relationship between the transportation capacity and the carbon emissions of the transport sector in a region. ③ Transportation capacity reduced TCE through economic level with a coefficient of 0.204%, but the opposite result was observed for the level of investment in the transportation sector. ④ The effect of transportation capacity on carbon emissions varied widely in different regions of China. Those of North and Central were significantly negative, and that of Central was consistent with the national level. The research results can provide reference to formulate differentiated policies for different regions and achieve the carbon peaking and carbon neutrality goals.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 11","pages":"6392-6402"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Analysis of Carbon Emission Reduction Potential and Discussion on the Green Development Path in Gansu-Qinghai Regions].","authors":"Li-Na Liu, Feng Gao, Jian-Sheng Qu, Pei-Qing Zhao, Chang-Liang Yin, Hua-Kun Zhou, Bao Wang, Zhong-Hua Zhang","doi":"10.13227/j.hjkx.202312058","DOIUrl":"https://doi.org/10.13227/j.hjkx.202312058","url":null,"abstract":"<p><p>The energy resources are rich, and the ecological environment is fragile in Gansu-Qinghai regions, which are facing problems in the coordinated development of green as well as low carbon transformation and high-quality economy. Based on the reality of Gansu-Qinghai regions, this study deeply analyzed the characteristics of regional carbon emissions； constructed the system dynamics model between carbon emissions and population, economy, energy, and policy； clarified the relationship between them； and probed into the future green development path. The results showed that： ① In recent years, the total and per capita carbon emissions in Gansu-Qinghai regions have been on the rise. From the perspective of energy structure, coal consumption was the most important source of carbon emissions, and the industrial sector had the greatest contribution from the point of view of sector contribution. ② Compared with the baseline scenario, by 2030, carbon emissions of Gansu Province could be reduced by 14% and 25%, and those of Qinghai Province could be reduced by 26% and 38% under the optimized and strengthened scenarios, respectively. ③ Compared with the optimization scenario, by 2030, carbon emissions of Gansu Province could be reduced by 5.39%, 3.53%, 2.74%, and 0.74%, and those of Qinghai Province could be reduced by 7.43%, 5.67%, 2.89%, and 0.26% under the scenarios of structural, scale, technological, and awareness strengthening, respectively. ④ According to the resource endowment of Gansu-Qinghai regions, strengthening policies to promote green and low-carbon development, accelerating industrial transformation and upgrading to help high-quality development, and promoting the coordinated development of ecological protection and pollution reduction will help to promote the realization of \"double carbon.\"</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 11","pages":"6354-6364"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Reversal Process and Driving Force Analysis for Arable Land Ecological Quality in Ningbo Using Time-series Remote Sensing Technology].","authors":"Ming Hu, Chao Sun, Sai-Shuai Zhao, Shu Zhang, Xing-Ru Shen, Ke Shi","doi":"10.13227/j.hjkx.202311234","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311234","url":null,"abstract":"<p><p>Timely monitoring of the changes in the ecological quality of arable land and the driving forces is of great significance for maintaining the ecological balance and sustainable development of agriculture. This study used the advanced time-series remote sensing continuous change detection and classification （CCDC） algorithm to synthesize images with the acquisition date of each year, in order to overcome the impacts of cloudy weather and vegetation phenology. Based on this, the reversal process and mechanism for the ecological quality of arable land in Ningbo were precisely identified using the comprehensive ecological evaluation index （CEEI） and geo-detector methods. The results showed that：① With a key turning point of the year 2014, the ecological quality of the arable land in Ningbo experienced a rapid rebound after a long-term decline, represented by the average CEEI decreasing from 0.649 to 0.617 and rising to 0.628. Until 2019, the ecological quality had recovered to the level of that in 2003. This reverse in the ecological quality of the arable land for each district successively appeared from 2011 to 2015, the northern area of Ningbo （i.e., the town center, Yuyao, and Cixi） presented a restored trend after first degraded, while the southern Ningbo area （i.e., Fenghua, Ninghai, and Xiangshan） presented an improved trend after long-term maintenance. ② The dominant driving force of the ecological quality of the arable land in Ningbo presented a reversal that it first converted from the rural labor resource （the period： 1990-1994） to the irrigated area or rural fertilizer usage （the period： 1995-2014） and then converted to the rural labor resource （the period： 2015-2019） again. The maintenance of the rural labor resource and the improvement in the level of agricultural mechanization in the past 5 years facilitated the implementation of land consolidation and high-standard farmland development, which directly promoted the reversal process. Such fundamental and key effects of the rural labor resource were particularly outstanding for the town center and Cixi. The study can provide technical reference for accurate monitoring of the ecological quality for coastal cities, and the related findings are expected to serve for the effective management of arable land resources and high-quality development of agriculture.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 11","pages":"6501-6513"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Synergistic Emission Reduction of Carbon Dioxide and Atmospheric Pollutants Under Different Low-carbon Development Scenarios of the Power Industry in Jiangsu Province].","authors":"Xiao-Wen Xing, Lin Huang, Jian-Lin Hu","doi":"10.13227/j.hjkx.202311231","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311231","url":null,"abstract":"<p><p>The power industry is the main source of carbon dioxide （CO₂） emissions in Jiangsu Province and also an important source of sulfur dioxide （SO₂）, nitrogen oxides （NO<i>_x</i>）, and particulate matter （PM）. In order to address climate change and contribute to the goal of \"carbon peaking and carbon neutrality,\" Jiangsu Province has implemented a series of low-carbon development policies in the power industry. These policies not only reduce carbon emissions but also have important synergistic emission reduction benefits for atmospheric pollutants. Based on the low-carbon development plan for electricity in Jiangsu Province, a baseline scenario （BAU） and four low-carbon development scenarios have been constructed： current policy scenario （CLE）, IEA target scenario （IEA）, accelerated coal-fired power phaseout scenario 1 （STE1）, and scenario 2 （STE2）. An econometric model was used to predict the future electricity demand in Jiangsu Province, and the greenhouse gas-air pollution interactions and synergies （GAINS） model was employed to quantitatively analyze the impact of low-carbon policies in the power sector on the emissions of CO₂, SO₂, NO<i>_x</i>, and PM, which are the major air pollutants in the region. The results showed that the electricity demand in Jiangsu Province has been increasing year by year, with an annual growth rate of approximately 4.01%. Under the BAU scenario, carbon emissions were projected to peak around 2030, with a peak carbon emission level of 462.03 Mt. Under the IEA scenario, it should reach its peak around 2028, with a peak emission level of 380.27 Mt. Under the CLE scenario, the peak would be expected to occur around 2026 at 353.46 Mt. In both STE1 and STE2 scenarios, carbon emissions had reached their peak and were continuously declining after 2020. In all scenarios, the replacement of conventional coal-fired power plants with natural gas （GAS）, nuclear power （NUC）, solar photovoltaic （SPV）, and wind power （WND） showed high synergistic benefits in pollution reduction and carbon reduction. The deployment of biomass energy （OS1） and non-renewable waste energy （OS2） will result in a significant increase in SO₂ emissions. Carbon capture and storage （CCS） transformation of coal-fired power only showed significant synergistic benefits after 2035. The development of OS1 and OS2 fuel substitutes in power plants should focus more on reducing SO₂ emissions, while upgrading and retrofitting CCS technology should prioritize the reduction of particulate matter emissions. The research findings provide a reference and decision-making basis for the synergistic efficiency of pollution reduction and carbon reduction in the power industry in Jiangsu Province.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 11","pages":"6326-6335"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Impacts of Urbanization on Soil Aggregate Stability and Organic Carbon Content in Urban Greenspaces: A Case Study of Nanchang City, Jiangxi Province].","authors":"Fo-Yi Zhang, Chang-Yong-Ming Cai, Jia-Lin Zhong, Fei Huang, Xin Li, Xin-Yan Li, Wei Liu, Qiong Wang","doi":"10.13227/j.hjkx.202312268","DOIUrl":"https://doi.org/10.13227/j.hjkx.202312268","url":null,"abstract":"<p><p>Exploring the mechanisms of the impacts of urbanization on soil aggregate stability and soil organic carbon （SOC） content will contribute to improving soil quality in urban greenspaces. Using the built-up area of Nanchang City, Jiangxi Province as a case study, the urbanization intensity was differentiated by impervious rate, and the vegetation characteristics and soil properties of 184 greenspace plots were investigated and determined. Variations in the stability parameters （geometric mean diameter, mean weight diameter, fractal dimension, and unstable aggregate index） and SOC contents across soil aggregate-size fractions （&gt;2, 1-2, 0.25-1, 0.053-0.25, and &lt;0.053 mm） and their interaction mechanisms with soil physicochemical properties and vegetation characteristics were analyzed in different urbanization intensities. The results showed that： ① The mass fractions of 0.053-0.25 mm aggregates in low urbanization areas were significantly lower than that in medium and high urbanization areas （<i>P</i>&lt;0.05）, whereas there was no significant difference in soil aggregate stability among different urbanization intensities （<i>P</i>&gt;0.05）. ② The SOC contents of &gt;2, 1-2, 0.25-1, and 0.053-0.25 mm aggregates were significantly higher than that in high urbanization areas by 26%-39% （<i>P</i>&lt;0.05）, while the SOC contents of &lt;0.053 mm aggregates were not affected by urbanization （<i>P</i>&gt;0.05）. ③ Both redundancy analysis and structural equation modeling demonstrated that urbanization influenced the changes in soil physicochemical properties （decreasing total nitrogen and phosphorus and increasing bulk density）, which indirectly reduced SOC accumulation of aggregates, whereas the larger tree height, diameter at breast height, crown diameter, diversity index, and herb coverage could directly or indirectly improve SOC content and the stability of aggregates. In conclusion, although urbanization indirectly decreased the SOC contents of aggregates, the aggregate stability was not affected by it. The manipulation of soil physicochemical properties and vegetation characteristics could alleviate the negative effects of urbanization on the SOC accumulation of aggregates, which provides a theoretical foundation for improving soil quality in urban greenspaces.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"45 11","pages":"6538-6545"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Comparative Life Cycle Assessment and Carbon Footprint of Typical Hydrogen Energy Products].","authors":"Xiao-Yu Huang, Ming-Hui Xie, Xiao-Wei Li, Le-Yong Jiang","doi":"10.13227/j.hjkx.202311004","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311004","url":null,"abstract":"<p><p>To compare the environmental impact and carbon footprint of gray hydrogen, blue hydrogen, and green hydrogen, inventories were obtained through literature research. Some inventories that were not available in China were obtained through foreign inventories combined with localized power conversion. The localized end-point destructive life cycle impact assessment method was used to calculate the environmental impact potential of the raw material acquisition, transportation, and hydrogen production stages of five hydrogen products. The carbon footprint was calculated, and the sensitivity analysis and uncertainty analysis were carried out and compared with the ReCiPe method. The results showed that： ① The environmental impact from large to small was： gray hydrogen （coal） （1 203 mPt·kg^-1） &gt; blue hydrogen （coal） （876 mPt·kg^-1） &gt; gray hydrogen （gas） （492 mPt·kg^-1） &gt; green hydrogen （323 mPt·kg^-1） &gt; blue hydrogen （gas） （252 mPt·kg^-1）. The environmental impacts of gray hydrogen and blue hydrogen were mainly concentrated in climate change, fine particulate matter formation, and fossil fuels. The environmental impacts of green hydrogen were mainly concentrated in climate change, fine particulate matter formation, fossil fuels, and mineral resources. ② The carbon footprint from large to small was： gray hydrogen （coal） （23.79 kg·kg^-1, measured by CO₂eq, the same below） &gt; blue hydrogen （coal） （11.07 kg·kg^-1） &gt; gray hydrogen （gas） （10.97 kg·kg^-1） &gt; blue hydrogen （gas） （3.47 kg·kg^-1） &gt; green hydrogen （1.97 kg·kg^-1）. Direct carbon emissions in the production process of gray hydrogen and blue hydrogen accounted for the largest proportion, whereas that of green hydrogen accounted for a large proportion of power input. ③ Measures to reduce environmental impact and carbon emissions include reducing direct emissions of pollutants and greenhouse gases, reducing power consumption, and strengthening raw material substitution and reduction.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 10","pages":"5641-5649"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Evaluation of Land Ecological Status and Diagnosis of Obstacle Factors in Jiangsu, China].","authors":"Qing-Ke Yang, Lei Wang, Li-Gang Lü, Ying Li, Ye-Ting Fan, Gao-Li Zhu, Ya-Zhu Wang","doi":"10.13227/j.hjkx.202311246","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311246","url":null,"abstract":"<p><p>By constructing a land ecological evaluation index system at the village scale and using models such as spatial correlation analysis, hotspot analysis, and obstacle factor diagnosis, the basic characteristics, spatial differentiation, and obstacle factors of land ecological status in Jiangsu Province were studied. This study sought to clarify the foundation, structure, function, and benefit characteristics of land ecosystems and optimize land management and policy regulation. The results showed that： ① The spatial distribution of land ecological status in Jiangsu Province was high in the north and low in the south, with multiple high-value areas radiating outward and decreasing, with low value centers radiating outward and increasing. The distribution area of the highest and lower values was relatively small, whereas the area of the middle value area was the largest. The higher values were mainly distributed in the suburbs and edge areas of each county. ② The spatial autocorrelation of land ecological status in Jiangsu Province was significant, with hot spots mainly concentrated in northern Jiangsu and cold spots concentrated in southern Jiangsu, as well as some areas of Taizhou and Nantong. The spatial distribution of cold and hot spots showed a complementary pattern with the level of regional development. The comprehensive index value of land ecology in developed areas was lower, whereas the index value in underdeveloped areas was higher. ③ The natural background conditions of Class Ⅰ land ecological zone in Jiangsu Province were superior, with good ecological construction and benefits and a high level of ecological status. The obstacle factors mainly included the proportion of water bodies and the average annual degradation rate of forest land. The Class Ⅱ land ecological zone was mostly located in the Huainan region and mainly composed of plain landforms. The Class Ⅲ land ecological zone had the largest area, located in the riverside areas of southern Jiangsu. The obstacle factors mainly included the average annual degradation rate of arable land and the proportion of soil pollution area. By controlling land ecological risks, the early warning level of ecological crisis could be improved.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 10","pages":"5880-5889"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Structural Characteristics of Phytoplankton Communities and Its Relationship with Environmental Factors in Different Habitats of Hedi Reservoir].","authors":"Rui-Xin Sun, Li Xu, Rong-Chang Liang, Qi-Jia Cai, Qian-Li Ma, Zheng-Yan Geng, Xing-Zhou Lin, Yu-Yin Yang, Ling-Ai Yao, Rui Zhao","doi":"10.13227/j.hjkx.202311178","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311178","url":null,"abstract":"<p><p>To explore the characteristics of phytoplankton communities and their relationship with environmental factors in different habitats of Hedi Reservoir, the inflow rivers, estuaries, and reservoir area of Hedi Reservoir were investigated in February （recession period）, April （flood period）, July （flood period）, and December （recession period） of 2022. During the investigation, 231 species of phytoplankton that belong to seven phyla were identified, and the cell density of phytoplankton ranged from 2.94 × 10⁶ - 8.04 × 10⁸ cells·L^-1. Phytoplankton cell density in flood periods were higher than that in recession periods, and that was higher in estuaries and the reservoir area than that in inflow rivers. Meanwhile, the cell density of phytoplankton in the estuarine and reservoir area was dominated by Cyanobacteria throughout the year, especially <i>Raphidiopsis raciborskii</i>, whereas the cell density of phytoplankton in inflow rivers was dominated by Cyanophyta, Chlorophyta, and Bacillariophyta. In the inflow river area, the dominant species of cyanobacteria were <i>Microcystis aeruginosa</i>, <i>Limnothrix redekei</i>, <i>Pseudanabaena circinalis,</i> and <i>Merismopedia punctata</i>； the dominant species of Chlorophyta were <i>Chlorella vulgaris</i> and <i>Crucigenia tetrapedia</i>； and the dominant species of Bacillariophyta were <i>Chlorella vulgaris</i> and <i>Melosira granulate</i>. The highest biodiversity （Shannon-Wiener Index, Pielou index, and Margalef index） were observed in the inflow river area of Hedi Reservoir. The correlation analysis （Pearson） indicated that the environmental factors that were significantly correlated to phytoplankton communities included water temperature, dissolved oxygen, pH, conductivity, nitrogen, and phosphorus concentration. The RDA analysis indicated that phytoplankton communities in the inflow river area were mainly affected by pH and total nitrogen concentration, which were majorly affected by water temperature and pH in the estuarine area and chiefly affected by turbidity and pH in the reservoir. The pH affected the changes in phytoplankton communities in all three different habitats, whereas the inflow river area was significantly affected by total nitrogen concentration, and the estuarine and reservoir were significantly affected by water temperature and turbidity, respectively.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 10","pages":"5822-5832"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Characteristics and Drivers of Soil Carbon, Nitrogen, and Phosphorus Ecological Stoichiometry at the Heavy Degradation Stage of the Alpine Meadow].","authors":"Yu-Ping Wu, Ming-Jun Ding, Hua Zhang, Yue-Ju Zhang, Huan Xu, Peng Huang","doi":"10.13227/j.hjkx.202310130","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310130","url":null,"abstract":"<p><p>An in-depth understanding of the soil nutrient status and balance relationship can help the effective recovery and management of alpine degraded meadows. In order to study the balance relationship among soil carbon, nitrogen, and phosphorus nutrients during the heavy degradation stage of meadows, field sampling and investigation, indoor analysis, and mathematical statistics were used to explore the characteristics and driving factors of changes in soil carbon, nitrogen, and phosphorus content, storage, and ecological stoichiometry during the heavy degradation stage of alpine meadows in the Sanjiangyuan region. The results showed that in the heavy degradation stage, miscellaneous grass plants occupied absolute dominance, soil C∶N∶P was approximately 32.83∶3.87∶0.67, and there was certain nitrogen limitation. The coefficients of variation of soil carbon, nitrogen, and phosphorus content were in the following order： organic carbon （1.09） &gt; total nitrogen （0.63） &gt; total phosphorus （0.29）. The organic carbon content and the carbon and nitrogen ratio showed a significant linear decreasing trend with the increase in the grassland degradation index （GDI）, while the total phosphorus content and organic carbon storage showed a significant non-linear change, in which the total phosphorus content showed a significant gentle U-shaped distribution, and the organic carbon storage decreased more gently at the beginning of the heavy degradation stage and then decreased sharply when the GDI was 57.9. The results of Mantel correlation analysis showed that the soil carbon to nitrogen ratio, carbon to phosphorus ratio, and nitrogen to phosphorus ratio showed significant correlation with organic carbon content and storage and total nitrogen storage. The results of structural equation modeling indicated that soil water content had direct effects as well as indirect through vegetation factors, soil carbon, nitrogen, and phosphorus ecological stoichiometry ratios, and soil water content and vegetation factors （height, cover, and biomass） were key environmental factors affecting soil ecological stoichiometry. The research results can provide scientific basis and practical guidance for the restoration of heavily degraded grassland in alpine meadows.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 10","pages":"6050-6060"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Comparative Study of Water Quality Prediction Methods Based on Different Artificial Neural Network].","authors":"Ming-Jun Xiao, Yi-Chun Zhu, Wen-Yuan Gao, Yu Zeng, Hao Li, Shuo-Fu Chen, Ping Liu, Hong-Li Huang","doi":"10.13227/j.hjkx.202310074","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310074","url":null,"abstract":"<p><p>The prediction of future data using existing data is an effective tool for regional planning and watershed management. The back propagation neural network （BPNN） and convolutional neural network （CNN） were used to construct a prediction model based on the water quality index of Hengyang in Xiangjiang River Basin from April to May 2022 and the results of permanganate index prediction by different models were compared. The prediction results displayed by BPNN could predict the water quality； however, overfitting occurred during the prediction. BPNN modified by particle swarm optimization （PSO） could avoid overfitting, which improved the parameter selection method of the BPNN mode. The CNN model had a better prediction effect, which had a more complex structure and a more scientific fitting method to avoid the model falling into the local extreme value during the fitting process and improve the accuracy of the model prediction results. The evaluation parameters including root-mean-square error （RMSE）, coefficient of determination （<i>R</i>²）, and mean absolute error （MAE） were used to predict the accuracy of the network. Compared with that of the traditional BPNN model, PSO-BPNN reduced the RESM of the test set from 0.278 2 mg·L^-1 to 0.210 9 mg·L^-1, reduced the MAE of the test set from 0.222 3 mg·L^-1 to 0.153 7 mg·L^-1 and increased the <i>R</i>² of the test set from 0.864 0 to 0.921 8, which indicated that PSO-BPNN had more stable fitting ability. RMSE, MAE, and <i>R</i>² of the test set in the CNN model were 0.122 0 mg·L^-1, 0.092 7 mg·L^-1, and 0.970 5, respectively, which showed that CNN had a better fitting and prediction effect than that of BPNN.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":"45 10","pages":"5761-5767"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}